Description

This curriculum spans the technical and operational rigor of a multi-workshop program focused on production-grade AI deployment, comparable to the internal capability building seen in enterprises establishing ML governance and MLOps at scale.

Module 1: Defining AI Product Requirements with Operational Constraints

Selecting model performance thresholds that balance accuracy with inference latency requirements for real-time systems.
Specifying data freshness SLAs based on downstream business process dependencies and retraining schedules.
Documenting model interpretability requirements for regulated industries during initial product scoping.
Identifying fallback mechanisms for model unavailability and defining acceptable degradation paths.
Mapping model inputs to available production data sources, including schema compatibility and access controls.
Establishing monitoring KPIs aligned with business outcomes, not just model metrics like AUC or RMSE.
Deciding whether to build custom models or integrate third-party APIs based on core competency and long-term maintenance.
Assessing hardware constraints (e.g., GPU vs CPU inference) during early architecture planning.

Module 2: Designing Scalable and Secure AI System Architecture

Selecting between synchronous and asynchronous inference patterns based on user experience and backend load.
Implementing secure model artifact storage with role-based access and audit logging in cloud object stores.
Isolating model inference workloads using containerization and network policies in Kubernetes.
Designing input validation layers to prevent adversarial inputs or schema drift at the API edge.
Choosing between serverless inference and dedicated model serving clusters based on traffic patterns.
Integrating feature stores with model serving to ensure training-serving consistency.
Configuring TLS termination and mutual authentication for inter-service communication involving model APIs.
Implementing circuit breakers and rate limiting to protect model endpoints from cascading failures.

Module 3: Model Versioning and Reproducibility Practices

Assigning immutable version identifiers to model artifacts, training code, and dataset snapshots.
Storing model lineage metadata including hyperparameters, training environment, and evaluation metrics.
Using container images to capture the full inference runtime environment for reproducibility.
Implementing model registry workflows with approval gates for promotion across environments.
Automating checksum validation of model weights upon deployment to prevent corruption.
Linking model versions to specific feature store schema versions to prevent drift.
Retaining historical model versions for rollback and A/B testing with clear retention policies.
Enforcing model signing and verification to prevent unauthorized model updates.

Module 4: Deployment Strategies for AI Models in Production

Implementing canary rollouts with traffic splitting to monitor model behavior under real load.
Configuring blue-green deployments for stateless model servers to minimize downtime.
Setting up shadow deployments to compare new model outputs against production without user impact.
Automating deployment rollback triggers based on anomaly detection in prediction distributions.
Coordinating model and feature pipeline deployments to avoid version mismatches.
Validating model performance on production-like data before full release using dark launch.
Managing stateful model deployments (e.g., online learning) with checkpoint synchronization.
Orchestrating batch model updates across multiple regions with dependency sequencing.

Module 5: Monitoring and Observability for AI Systems

Instrumenting model endpoints to capture prediction latency, error rates, and payload sizes.
Tracking data drift using statistical tests on input feature distributions with automated alerts.
Monitoring prediction output stability and detecting unexpected shifts in class probabilities.
Correlating model performance degradation with upstream data pipeline failures.
Implementing structured logging with trace IDs to debug end-to-end AI-powered transactions.
Setting up business metric dashboards that link model decisions to revenue or conversion outcomes.
Using model introspection tools to log feature importance scores for high-stakes predictions.
Establishing thresholds for stale model detection based on last retraining timestamp.

Module 6: Governance, Compliance, and Audit Readiness

Documenting model decisions for audit trails in regulated domains like finance or healthcare.
Implementing data retention and model purging workflows to comply with GDPR or CCPA.
Conducting bias assessments across demographic segments during pre-deployment review.
Enforcing approval workflows for model deployment based on risk tier classification.
Generating model cards that summarize performance, limitations, and intended use.
Logging model access and prediction requests for forensic investigations.
Integrating with enterprise identity providers for model API access control.
Archiving training data samples and model outputs to support regulatory inquiries.

Module 7: Continuous Retraining and Feedback Loops

Designing feedback pipelines to capture ground truth labels from user actions or manual review.
Scheduling retraining cycles based on data drift metrics rather than fixed intervals.
Implementing data quality checks in automated retraining pipelines to prevent model poisoning.
Versioning and storing training datasets for reproducible retraining runs.
Validating new model versions against holdout datasets before deployment consideration.
Managing dependencies between feature engineering jobs and model training workflows.
Using shadow models to evaluate retrained candidates before promotion.
Handling concept drift by incorporating time-based weighting in training data selection.

Module 8: Incident Response and Model Rollback Procedures

Classifying AI incidents by severity based on business impact and user exposure.
Executing model rollback using versioned artifacts and configuration management tools.
Diagnosing root cause of model degradation using logged inputs and prediction metadata.
Communicating model outages to stakeholders with estimated resolution timelines.
Restoring service using fallback heuristics or rule-based systems during model downtime.
Conducting post-mortems that include model, data, and infrastructure factors.
Updating monitoring rules to prevent recurrence of known failure patterns.
Validating rollback integrity by comparing outputs of previous stable model version.

Module 9: Cross-Functional Coordination and Handoff Processes

Defining SLAs between data engineering, ML, and DevOps teams for pipeline reliability.
Standardizing model handoff documentation including schema, dependencies, and test cases.
Conducting production readiness reviews with infrastructure, security, and compliance teams.
Aligning model release schedules with business stakeholders to avoid peak period disruptions.
Integrating model deployment into existing CI/CD pipelines with automated testing gates.
Coordinating incident response roles across ML, SRE, and support teams.
Establishing change advisory boards for high-risk model deployments.
Managing communication of model updates to downstream consumers via API versioning.